Never born proteins as a test case for ab initio protein structures prediction

The number of natural proteins although large is significantly smaller than the theoretical number of proteins that can be obtained combining the 20 natural amino acids, the so-called “never born proteins” (NBPs). The study of the structure and properties of these proteins allows to investigate the sources of the natural proteins being of unique characteristics or special properties. However the structural study of NPBs can also been intended as an ideal test for evaluating the efficiency of software packages for the ab initio protein structure prediction. In this research, 10.000 three-dimensional structures of proteins of completely random sequence generated according to ROSETTA and FOD model were compared. The results show the limits of these software packages, but at the same time indicate that in many cases there is a significant agreement between the prediction obtained.     

Minimally complex problem set for an<Emphasis Type="Italic">Ab Initio</Emphasis> protein structure prediction study

A “minimally complex problem set” forab initio protein structure prediction has been proposed. As well as consisting of non-redundant and crystallographically determined high-resolution protein structures, without disulphide bonds, modified residues, unusual connectivities and heteromolecules, it is more importantly a collection of protein structures, with a high probability of being the same in the crystal form as in solution. To our knowledge, this is the first attempt at this kind of dataset. Considering the lattice constraint in crystals, and the possible flexibility in solution of crystallographically determined protein structures, our dataset is thought to be the safest starting points for anab initio protein structure prediction study.     

Computational analyses of protein coded by rice (Oryza sativa japonica) cDNA (GI: 32984786) indicate lectin like Ca2+ binding properties for Eicosapenta Peptide Repeats (EPRs)

Eicosapenta peptide repeats (EPRs) occur exclusively in flowering plant genomes and exhibit very high amino acid residue conservation across occurrence. DNA and amino acid sequence searches yielded no indications about the function due to absence of similarity to known sequences. Tertiary structure of an EPR protein coded by rice (Oryza sativa japonica) cDNA (GI: 32984786) was determined based on ab initio methodology in order to draw clues on functional significance of EPRs. The resultant structure comprised of seven α-helices and thirteen anti-parallel β-sheets. Surface-mapping of conserved residues onto the structure deduced that (i) regions equivalent to β α4- the primary function of EPR protein could be Ca²⁺ binding, and (iii) the putative EPR Ca²⁺ binding domain is structurally similar to calcium-binding domains of plant lectins. Additionally, the phylogenetic analysis showed an evolving taxa-specific distribution of EPR proteins observed in some GNA-like lectins.     

Determining the three-dimensional fold of a protein from approximate constraints

We propose a new approach for calculating the three-dimensional (3D) structure of a protein from distance and dihedral angle constraints derived from experimental data. We suggest that such constraints can be obtained from experiments such as tritium planigraphy, chemical or enzymatic cleavage of the polypeptide chain, paramagnetic perturbation of nuclear magnetic resonance (NMR) spectra, measurement of hydrogen-exchange rates, mutational studies, mass spectrometry, and electron paramagnetic resonance. These can be supplemented with constraints from theoretical prediction of secondary structures and of buried/exposed residues. We report here distance geometry calculations to generate the structures of a test protein Staphylococcal nuclease (STN), and the HIV-1 rev protein (REV) of unknown structure. From the available 3D atomic coordinates of STN, we set up simulated data sets consisting of varying number and quality of constraints, and used our group's Self Correcting Distance Geometry (SECODG) program DIAMOD to generate structures. We could generate the correct tertiary fold from qualitative (approximate) as well as precise distance constraints. The root mean square deviations of backbone atoms from the native structure were in the range of 2.0 A to 8.3 A, depending on the number of constraints used. We could also generate the correct fold starting from a subset of atoms that are on the surface and those that are buried. When we used data sets containing a small fraction of incorrect distance constraints, the SECODG technique was able to detect and correct them. In the case of REV, we used a combination of constraints obtained from mutagenic data and structure predictions. DIAMOD generated helix-loop-helix models, which, after four self-correcting cycles, populated one family exclusively. The features of the energy-minimized model are consistent with the available data on REV-RNA interaction. Our method could thus be an attractive alternative for calculating protein 3D structures, especially in cases where the traditional methods of X-ray crystallography and multidimensional NMR spectroscopy have been unsuccessful.     

Machine learning in protein structure prediction

Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the increasing “neuralization” of structure prediction pipelines, whereby computations previously based on energy models and sampling procedures are replaced by neural networks. The extraction of physical contacts from the evolutionary record; the distillation of sequence–structure patterns from known structures; the incorporation of templates from homologs in the Protein Databank; and the refinement of coarsely predicted structures into finely resolved ones have all been reformulated using neural networks. Cumulatively, this transformation has resulted in algorithms that can now predict single protein domains with a median accuracy of 2.1 Å, setting the stage for a foundational reconfiguration of the role of biomolecular modeling within the life sciences.     

A critique of the utility of the prediction of protein secondary structure

The Chou-Fasman predictive algorithm for determining the secondary structure of proteins from the primary sequence is reviewed. Many examples of its use are presented which illustrate its wide applicability, such as predicting (a) regions with the potential for conformational change, (b) sequences which are capable of assuming several conformations in different environments, (c) effects of single amino acid mutations, (d) amino acid replacements in synthesis of peptides to bring about a change in conformation, (e) guide to the synthesis of polypeptides with definitive secondary structure,e.g. signal sequences, (f) conformational homologues from varying sequences and (g) the amino acid requirements for amphiphilicα-helical peptides.     

Modeling of the structural features of integral-membrane proteins reverse-environment prediction of integral membrane protein structure (REPIMPS)

The Profiles-3D application, an inverse-folding methodology appropriate for water-soluble proteins, has been modified to allow the determination of structural properties of integral-membrane proteins (IMPs) and for testing the validity of solved and model structures of IMPs. The modification, known as reverse-environment prediction of integral membrane protein structure (REPIMPS), takes into account the fact that exposed areas of side chains for many residues in IMPs are in contact with lipid and not the aqueous phase. This (1) allows lipid-exposed residues to be classified into the correct physicochemical environment class, (2) significantly improves compatibility scores for IMPs whose structures have been solved, and (3) reduces the possibility of rejecting a three-dimensional structure for an IMP because the presence of lipid was not included. Validation tests of REPIMPS showed that it (1) can locate the transmembrane domain of IMPs with single transmembrane helices more frequently than a range of other methodologies, (2) can rotationally orient transmembrane helices with respect to the lipid environment and surrounding helices in IMPs with multiple transmembrane helices, and (3) has the potential to accurately locate transmembrane domains in IMPs with multiple transmembrane helices. We conclude that correcting for the presence of the lipid environment surrounding the transmembrane segments of IMPs is an essential step for reasonable modeling and verification of the three-dimensional structures of these proteins.     

Assessment of putative protein targets derived from the SARS genome

The ability to rapidly and reliably develop hypotheses on the function of newly discovered protein sequences requires systematic and comprehensive analysis. Such an analysis, embodied within the DS GeneAtlas pipeline, has been used to critically evaluate the severe acute respiratory syndrome (SARS) genome with the goal of identifying new potential targets for viral therapeutic intervention. This paper discusses several new functional hypotheses on the roles played by the constituent gene products of SARS, and will serve as an example of how such assignments can be developed or extended on other systems of interest.     

PackHelix: a tool for helix-sheet packing during protein structure prediction

The three‐dimensional structure of a protein is organized around the packing of its secondary structure elements. Predicting the topology and constructing the geometry of structural motifs involving α‐helices and/or β‐strands are therefore key steps for accurate prediction of protein structure. While many efforts have focused on how to pack helices and on how to sample exhaustively the topologies and geometries of multiple strands forming a β‐sheet in a protein, there has been little progress on generating native‐like packings of helices on sheets. We describe a method that can generate the packing of multiple helices on a given β‐sheet for αβα sandwich type protein folds. This method mines the results of a statistical analysis of the conformations of αβ₂ motifs in protein structures to provide input values for the geometric attributes of the packing of a helix on a sheet. It then proceeds with a geometric builder that generates multiple arrangements of the helices on the sheet of interest by sampling through these values and performing consistency checks that guarantee proper loop geometry between the helices and the strands, minimal number of collisions between the helices, and proper formation of a hydrophobic core. The method is implemented as a module of ProteinShop. Our results show that it produces structures that are within 4–6 Å RMSD of the native one, regardless of the number of helices that need to be packed, though this number may increase if the protein has several helices between two consecutive strands in the sequence that pack on the sheet formed by these two strands. Proteins 2011; Published 2011 Wiley‐Liss, Inc.     

Comparison of protein quantification and extraction methods suitable for E. coli cultures

Many different extraction and analysis methods exist to determine the protein fraction of microbial cells. For metabolic engineering purposes it is important to have precise and accurate measurements. Therefore six different protein extraction protocols and seven protein quantification methods were tested and compared. Comparison was based on the reliability of the methods and boxplots of the normalized residuals. Some extraction techniques (SDS/chloroform and toluene) should never be used: the measurements are neither precise nor accurate. Bugbuster extraction combined with UV280 quantification gives the best results, followed by the combinations Sonication-UV280 and EasyLyse-UV280. However, if one does not want to use the quantification method UV280, one can opt to use Bugbuster, EasyLyse or sonication extraction combined with any quantification method with exception of the EasyLyse-BCA_P and Sonication-BCA_P combinations.     

Proteomics-based consensus prediction of protein retention in a bacterial membrane

The availability of complete bacterial genome sequences allows proteome-wide predictions of exported proteins that are potentially retained in the cytoplasmic membranes of the corresponding organisms. In practice, however, major problems are encountered with the computer-assisted distinction between (Sec-type) signal peptides that direct exported proteins into the growth medium and lipoprotein signal peptides or amino-terminal membrane anchors that cause protein retention in the membrane. In the present studies, which were aimed at improving methods to predict protein retention in the bacterial cytoplasmic membrane, we have compared sets of membrane-attached and extracellular proteins of Bacillus subtilis that were recently identified through proteomics approaches. The results showed that three classes of membrane-attached proteins can be distinguished. Two classes include 43 lipoproteins and 48 proteins with an amino-terminal transmembrane segment, respectively. Remarkably, a third class includes 31 proteins that remain membrane-retained despite the presence of typical Sec-type signal peptides with consensus signal peptidase recognition sites. This unprecedented finding indicates that unknown mechanisms are involved in membrane retention of this class of proteins. A further novelty is a consensus sequence indicative for release of certain lipoproteins from the membrane by proteolytic shaving. Finally, using non-overlapping sets of secreted and membrane-retained proteins, the accuracy of different signal peptide prediction algorithms was assessed. Accuracy for the prediction of protein retention in the membrane was increased to 82% using a majority-vote approach. Our findings provide important leads for future identification of surface proteins from pathogenic bacteria, which are attractive candidate infection markers and potential targets for drugs or vaccines.     

去垢剂在膜蛋白研究中的应用

去垢剂是同时具有亲水极性基团和疏水非极性基团的双极性分子,能够使脂膜解体释放膜蛋白,并在溶液中为去膜状态下的膜蛋白提供疏水环境,维持和保护膜蛋白的疏水跨膜结构,在膜蛋白的结构和功能研究中有重要的意义。去垢剂的双极性和理化特性,如临界胶束浓度能够极大影响去垢剂和膜蛋白间的相互作用。在膜蛋白研究中,需要充分利用去垢剂的结构和特性:一方面,需要利用去垢剂代替脂质分子支持和稳定去膜状态下膜蛋白的结构和功能;另一方面,需要控制去垢剂和膜蛋白的相互作用,以满足膜蛋白结构研究如蛋白质结晶试验的要求。简要介绍了去垢剂在膜蛋白研究中的最新应用进展,涉及去垢剂在膜蛋白离体表达、分离和纯化、以及结构研究中的应用。     

蛋白质结构预测的理论方法及阶段

一直以来,蛋白质结构预测都是人们研究的焦点,综述了蛋白质结构预测的几种理论方法和不同阶段。     

Structure prediction of the RPE65 protein

The RPE65 protein is located in the retinal pigment epithelial cells and plays an important role in the visual cycle. Although numerous experimental results demonstrate that it participates in the visual cycle, its detailed structure and function are not clear yet because of difficulties in isolation and crystallization. This paper describes a computational modeling study to propose a three-dimensional (3D) structure and suggest a possible mechanism for the function of the protein. The 3D-PSSM server is used to obtain the preliminary 3D structural model of the RPE65 protein. The coordinates of the side chains are obtained from the SCWRL program. Finally, two software packages, Jackal and Tinker with the CHARMM force field are used to fix and refine the preliminary structural model. Based on the obtained 3D structural model, a possible mechanism for the protein function is discussed.     

Membrane topology of gp41 and amyloid precursor protein: Interfering transmembrane interactions as potential targets for HIV and Alzheimer treatment

The amyloid precursor protein (APP), that plays a critical role in the development of senile plaques in Alzheimer disease (AD), and the gp41 envelope protein of the human immunodeficiency virus (HIV), the causative agent of the acquired immunodeficiency syndrome (AIDS), are single-spanning type-1 transmembrane (TM) glycoproteins with the ability to form homo-oligomers. In this review we describe similarities, both in structural terms and sequence determinants of their TM and juxtamembrane regions. The TM domains are essential not only for anchoring the proteins in membranes but also have functional roles. Both TM segments contain GxxxG motifs that drive TM associations within the lipid bilayer. They also each possess similar sequence motifs, positioned at the membrane interface preceding their TM domains. These domains are known as cholesterol recognition/interaction amino acid consensus (CRAC) motif in gp41 and CRAC-like motif in APP. Moreover, in the cytoplasmic domain of both proteins other α-helical membranotropic regions with functional implications have been identified. Recent drug developments targeting both diseases are reviewed and the potential use of TM interaction modulators as therapeutic targets is discussed.     

Scoring function accuracy for membrane protein structure prediction

We perform a systematic examination of the ability of several different high-resolution, atomic-detail scoring functions to discriminate native conformations of loops in membrane proteins from non-native but physically reasonable, or "decoy," conformations. Decoys constructed from changing a loop conformation while keeping the remainder of the protein fixed are a challenging test of energy function accuracy. Nevertheless, the best of the energy functions we examined recognized the native structure as lowest in energy around half the time, and consistently chose it as a low-energy structure. This suggests that the best of present energy functions, even without a representation of the lipid bilayer, are of sufficient accuracy to give reasonable confidence in predictions of membrane protein structure. We also constructed homology models for each structure, using other known structures in the same protein family as templates. Homology models were constructed using several scoring functions and modeling programs, but with a comparable sampling effort for each procedure. Our results indicate that the quality of sequence alignment is probably the most important factor in model accuracy for sequence identity from 20-40%; one can expect a reasonably accurate model for membrane proteins when sequence identity is greater than 30%, in agreement with previous studies. Most errors are localized in loop regions, which tend to be found outside the lipid bilayer. For the most discriminative energy functions, it appears that errors are most likely due to lack of sufficient sampling, although it should be stressed that present energy functions are still far from perfectly reliable.     

Genome-wide Membrane Protein Structure Prediction

Transmembrane proteins allow cells to extensively communicate with the external world in a very accurate and specific way. They form principal nodes in several signaling pathways and attract large interest in therapeutic intervention, as the majority pharmaceutical compounds target membrane proteins. Thus, according to the current genome annotation methods, a detailed structural/functional characterization at the protein level of each of the elements codified in the genome is also required. The extreme difficulty in obtaining high-resolution three-dimensional structures, calls for computational approaches. Here we review to which extent the efforts made in the last few years, combining the structural characterization of membrane proteins with protein bioinformatics techniques, could help describing membrane proteins at a genome-wide scale. In particular we analyze the use of comparative modeling techniques as a way of overcoming the lack of high-resolution three-dimensional structures in the human membrane proteome.     

Assessment of protein side‐chain conformation prediction methods in different residue environments

Computational prediction of side‐chain conformation is an important component of protein structure prediction. Accurate side‐chain prediction is crucial for practical applications of protein structure models that need atomic‐detailed resolution such as protein and ligand design. We evaluated the accuracy of eight side‐chain prediction methods in reproducing the side‐chain conformations of experimentally solved structures deposited to the Protein Data Bank. Prediction accuracy was evaluated for a total of four different structural environments (buried, surface, interface, and membrane‐spanning) in three different protein types (monomeric, multimeric, and membrane). Overall, the highest accuracy was observed for buried residues in monomeric and multimeric proteins. Notably, side‐chains at protein interfaces and membrane‐spanning regions were better predicted than surface residues even though the methods did not all use multimeric and membrane proteins for training. Thus, we conclude that the current methods are as practically useful for modeling protein docking interfaces and membrane‐spanning regions as for modeling monomers. Proteins 2014; 82:1971–1984. © 2014 Wiley Periodicals, Inc.     

Computational methods in protein structure prediction

This review presents the advances in protein structure prediction from the computational methods perspective. The approaches are classified into four major categories: comparative modeling, fold recognition, first principles methods that employ database information, and first principles methods without database information. Important advances along with current limitations and challenges are presented.